MLOps Roadmap
This MLOps Roadmap is a free, full-text syllabus of 62 lessons in 13 stages, starting with What is MLOps? and ending with Explainable AI. It suits anyone working towards a MLOps role, from a first-year student to a working developer changing tracks. There is no fixed length: every lesson is open from the start, so you go at your own pace and skip what you already know.
Last updated
Reading is open to everyone. Sign in to join this roadmap and track what you finish.
Continue learning
1. What is MLOps?What is MLOps?
0%What is MLOps?
10 topics · 9 sub-topics · 0 completed
The diagram draws the same 62 lessons as a map, with lines showing the order. It needs JavaScript; the full syllabus below lists every lesson as text.
Full syllabus: every lesson in the MLOps roadmap
13 stages and 62 lessons, in the order the roadmap suggests. Every lesson is free to read and open from the start.
Stage 1: What is MLOps?
What is MLOps?
MLOps, short for Machine Learning Operations, is a set of practices that aims to reliably and efficiently deploy and maintain machine learning models in production. It bridges the gap between model development (Data Scientists) and model deployment (Operations), emphasizing automation, monitoring, and collaboration…
MLOps Principles
MLOps (Machine Learning Operations) principles focus on streamlining the deployment, monitoring, and management of machine learning models in production environments. Key principles include:
MLOps Components
MLOps components can be broadly classified into three major categories: Development, Operations and Governance. The Development components include everything involved in the creation of machine learning models, such as data extraction, data analysis, feature engineering, and machine learning model training. The…
Version Control
Version control is a system that records changes to a file or set of files over time so that you can recall specific versions later. It allows multiple people to collaborate on the same project without overwriting each other's work. This system tracks modifications to code, configurations, data, and other…
CI/CD
CI/CD, which stands for Continuous Integration and Continuous Delivery/Deployment, is a software development practice that automates the process of building, testing, and deploying code changes. Continuous Integration focuses on frequently merging code changes into a central repository, followed by automated builds…
Orchestration
ML orchestration refers to the process of managing and coordinating the various tasks and workflows involved in the machine learning lifecycle, from data preparation and model training to deployment and monitoring. It involves integrating multiple tools and platforms to streamline operations, automate repetitive…
Experiment Tracking
Experiment Tracking is an essential part of MLOps, providing a system to monitor and record the different experiments conducted during the machine learning model development process. This involves capturing, organizing and visualizing the metadata associated with each experiment, such as hyperparameters used,…
Data Lineage
Data Lineage refers to the life-cycle of data, including its origins, movements, characteristics and quality. It's a critical component in MLOps for tracking the journey of data through every process in a pipeline, from raw input to model output. Data lineage helps in maintaining transparency, ensuring compliance,…
Model Training & Serving
Model Training refers to the phase in the Machine Learning (ML) pipeline where we teach a machine learning model how to make predictions by providing it with data. This process begins with feeding the model a training dataset, which it uses to learn and understand patterns or perform computations. The model's…
Monitoring & Observability
Monitoring in MLOps primarily involves tracking the performance of machine learning (ML) models in production to ensure that they continually deliver accurate and reliable results. Such monitoring is necessary because the real-world data that these models handle may change over time, a scenario known as data drift.…
Stage 2: Version Control Systems
Version Control Systems
Version control systems are tools that track changes to files over time. They allow multiple people to work on the same project simultaneously without overwriting each other's work. These systems record a history of modifications, enabling users to revert to previous versions, compare changes, and understand who…
Bash
Bash (Bourne Again Shell) is a Unix shell and command language used for interacting with the operating system through a terminal. It allows users to execute commands, automate tasks via scripting, and manage system operations. As the default shell for many Linux distributions, it supports command-line utilities,…
Python
Python is a widely used programming language known for its clear syntax and extensive libraries. It's a versatile tool that can handle many tasks, from simple scripting to complex software development. Its ease of use and the availability of specialized libraries for data analysis, machine learning, and automation…
SQL
SQL, or Structured Query Language, is a standard language for managing and manipulating data held in relational database management systems (RDBMS). It allows users to define, access, and control data, enabling operations like creating databases, inserting, updating, deleting, and retrieving data based on specific…
Go
Go, also known as Golang, is an open-source programming language developed by Google that emphasizes simplicity, efficiency, and strong concurrency support. Designed for modern software development, Go features a clean syntax, garbage collection, and built-in support for concurrent programming through goroutines…
Git
Git is a distributed version control system used to track changes in source code during software development. It enables multiple developers to collaborate on a project by managing versions of code, allowing for branching, merging, and tracking of revisions. Git ensures that changes are recorded with a complete…
GitHub
GitHub is a web-based platform built on top of Git that provides version control, collaboration tools, and project management features for software development. It enables developers to host Git repositories, collaborate on code through pull requests, and review and track changes. GitHub also offers additional…
DVC
DVC (Data Version Control) is an open-source tool designed to bring version control principles to machine learning projects, specifically for data and models. It extends Git's capabilities to handle large files, datasets, and machine learning models, which are typically not well-suited for traditional version…
Stage 3: Programming Fundamentals
Programming Fundamentals
ML programming fundamentals encompass the essential skills and concepts needed to develop machine learning models effectively. Key aspects include understanding data structures and algorithms, as well as proficiency in programming languages commonly used in ML, such as Python and R. Familiarity with libraries and…
Stage 4: CI/CD
CI/CD
CI/CD, which stands for Continuous Integration and Continuous Delivery/Deployment, is a software development practice focused on automating and streamlining the process of building, testing, and releasing software changes. Continuous Integration involves frequently merging code changes into a central repository,…
GItLab
GitLab is a web-based DevOps platform that provides a single application for all stages of the software development lifecycle. It offers features like source code management (using Git), CI/CD pipelines, issue tracking, and project management. GitLab allows teams to collaborate on code, automate build, test, and…
Jenkins
Jenkins is an open-source automation server that helps automate the software development processes, including building, testing, and deploying code. It provides a platform for continuous integration and continuous delivery (CI/CD), allowing teams to automate repetitive tasks and streamline their workflows. Jenkins…
GitHub Actions
GitHub Actions is a continuous integration and continuous delivery (CI/CD) platform that allows you to automate your software development workflows directly in your GitHub repository. You can use it to build, test, and deploy your code, as well as automate other tasks like managing issues and pull requests.…
CML
Continuous Machine Learning (CML) is a tool designed to bring continuous integration and continuous delivery (CI/CD) principles to machine learning projects. It allows data scientists and machine learning engineers to automate the process of training, evaluating, and deploying machine learning models. CML…
Stage 5: Machine Learning Fundamentals
Machine Learning Fundamentals
Machine learning fundamentals encompass the key concepts and techniques that enable systems to learn from data and make predictions or decisions without being explicitly programmed. At its core, machine learning involves algorithms that can identify patterns in data and improve over time with experience. Key areas…
Stage 6: Cloud Computing
Cloud Computing
Cloud Computing refers to the delivery of computing services over the internet rather than using local servers or personal devices. These services include servers, storage, databases, networking, software, analytics, and intelligence. Cloud Computing enables faster innovation, flexible resources, and economies of…
AWS / Azure / GCP
AWS (Amazon Web Services), Azure, and GCP (Google Cloud Platform) are three leading providers of cloud computing services. AWS by Amazon is the oldest and the most established among the three, providing a breadth and depth of solutions ranging from infrastructure services like compute, storage, and databases to…
Cloud-native ML Services
Cloud-native ML services are pre-built machine learning tools and platforms offered by cloud providers. These services allow users to build, train, and deploy machine learning models without managing the underlying infrastructure. They often include features like automated model training, scalable deployment…
Docker
Docker is a platform that uses operating system-level virtualization to deliver software in packages called containers. These containers isolate software from its environment and ensure that it works uniformly despite differences between development and production environments. Docker simplifies the process of…
Kubernetes
Kubernetes is an open-source system for automating the deployment, scaling, and management of containerized applications. It groups containers that make up an application into logical units for easy management and discovery. By orchestrating containers across multiple machines, Kubernetes ensures high availability…
Maths & Statistics
Mathematics and statistics provide the foundational principles for understanding and building machine learning models. These disciplines offer the tools to analyze data, quantify uncertainty, and optimize model performance. Key areas include linear algebra for data representation and manipulation, calculus for…
Machine Learning
Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed. The name machine learning was coined in 1959 by Arthur Samuel. Evolved…
Deep Learning
Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers (hence "deep") to analyze data with complex structures. These networks learn hierarchical representations of data, where each layer extracts increasingly abstract features from the previous layer. This allows…
Model Evaluation
Model evaluation is the process of assessing the performance of a machine learning model using various metrics and techniques. It helps determine how well the model generalizes to unseen data and whether it meets the desired performance criteria. This involves using different evaluation metrics depending on the…
Stage 7: Containerization
Containerization
Containerization is a form of operating system virtualization that packages an application and its dependencies into a single, isolated unit called a container. This container includes everything the application needs to run, such as code, runtime, system tools, libraries, and settings. Containers offer a…
Stage 8: Data Engineering Fundamentals
Data Engineering Fundamentals
Data Engineering is essentially dealing with the collection, validation, storage, transformation, and processing of data. The objective is to provide reliable, efficient, and scalable data pipelines and infrastructure that allow data scientists to convert data into actionable insights. It involves steps like data…
Terraform
Terraform is an open-source infrastructure as code (IaC) tool that allows you to define and provision infrastructure using a declarative configuration language. It enables you to manage infrastructure resources across various cloud providers and on-premises environments in a consistent and automated manner.…
Ansible
Ansible is an open-source automation tool used to configure systems, deploy software, and orchestrate more advanced IT tasks. It uses a simple, human-readable language (YAML) to define automation tasks, called playbooks. Ansible works by connecting to nodes (servers, virtual machines, etc.) and pushing out small…
Stage 9: Infrastructure as Code
Infrastructure as Code
Infrastructure as Code (IaC) is a modern approach to managing and provisioning IT infrastructure through machine-readable configuration files, rather than manual processes. It enables developers and operations teams to define and manage infrastructure resources—such as servers, networks, and databases—using code,…
Stage 10: Orchestration & Deployment
Orchestration & Deployment
Orchestration and deployment involve automating the process of taking a trained machine learning model and making it available for use in a production environment. This includes managing the workflow of model building, testing, and releasing, as well as handling the infrastructure needed to serve the model and…
Airflow
Airflow is a platform used to programmatically author, schedule, and monitor workflows. It allows you to define workflows as Directed Acyclic Graphs (DAGs) of tasks, where each task represents a unit of work. Airflow then executes these tasks in the specified order, handling dependencies, retries, and logging along…
KubeFlow
Kubeflow is an open-source machine learning platform designed to simplify the deployment and management of ML workflows on Kubernetes. It provides tools and components for building, training, and deploying machine learning models, allowing users to create portable and scalable ML pipelines. Kubeflow aims to make it…
Scikit-learn
Scikit-learn is a Python library that provides simple and efficient tools for data mining and data analysis. It features various classification, regression, clustering algorithms, and tools for model selection, preprocessing, and dimensionality reduction. It's built on NumPy, SciPy, and matplotlib, making it a…
TensorFlow
TensorFlow is an open-source software library created by Google for numerical computation and large-scale machine learning. It provides a comprehensive ecosystem of tools, libraries, and community resources that allows researchers and developers to build and deploy ML-powered applications. TensorFlow is…
PyTorch
PyTorch is an open-source machine learning framework primarily developed by Meta AI. It's used for a variety of applications, including computer vision, natural language processing, and reinforcement learning. PyTorch is known for its dynamic computation graph, which allows for more flexibility and easier debugging…
MLFlow
MLflow is an open-source platform designed to manage the complete machine learning lifecycle. It provides tools for tracking experiments, packaging code into reproducible runs, and deploying models to various platforms. MLflow helps data scientists and engineers streamline their workflows, collaborate effectively,…
Data Pipelines
Data pipelines are a series of automated processes that transport and transform data from various sources to a destination for analysis or storage. They typically involve steps like data extraction, cleaning, transformation, and loading (ETL) into databases, data lakes, or warehouses. Pipelines can handle batch or…
Data Lakes & Warehouses
Data lakes and data warehouses are both systems for storing large amounts of data, but they differ in structure and purpose. A data lake stores data in its raw, unprocessed format, allowing for flexibility in analysis and exploration. A data warehouse, on the other hand, stores data that has been structured and…
Data Ingestion Architecture
Data ingestion is the process of collecting, transferring, and loading data from various sources to a destination where it can be stored and analyzed. Several data ingestion architectures can be used to collect data from different sources and load it into a data warehouse, data lake, or other storage systems. These…
Stage 11: Monitoring & Observability
Monitoring & Observability
Monitoring and observability involve tracking the performance and health of machine learning models and the infrastructure they rely on. This includes gathering metrics, logs, and traces to understand how models are behaving in production, identify potential issues like performance degradation or data drift, and…
Prometheus
Prometheus is an open-source monitoring and alerting toolkit originally built at SoundCloud. It collects and stores metrics as time-series data, meaning metrics are stored with a timestamp at which they were recorded, along with optional key-value pairs called labels. Prometheus uses a pull model to scrape metrics…
Grafana
Grafana is an open-source data visualization and monitoring tool. It allows users to query, visualize, alert on, and explore metrics, logs, and traces. Grafana connects to various data sources, such as Prometheus, Graphite, Elasticsearch, and InfluxDB, to create customizable dashboards that display real-time data…
Spark
Apache Spark is an open-source distributed computing system designed for big data processing and analytics. It offers a unified interface for programming entire clusters, enabling efficient handling of large-scale data with built-in support for data parallelism and fault tolerance. Spark excels in processing tasks…
Kafka
Kafka is a distributed, fault-tolerant, high-throughput streaming platform. It's primarily used for building real-time data pipelines and streaming applications, allowing you to publish, subscribe to, store, and process streams of records. These streams can originate from various sources and be consumed by multiple…
Flink
Apache Flink is an open-source stream processing framework designed for real-time and batch data processing with low latency and high throughput. It supports event time processing, fault tolerance, and stateful operations, making it ideal for applications like real-time analytics, fraud detection, and event-driven…
Stage 12: Edge AI
Edge AI
Edge AI refers to running machine learning models directly on devices, like smartphones, sensors, or embedded systems, rather than relying on a central server or cloud infrastructure. This approach brings computation and data processing closer to the source of data generation. This enables faster response times,…
TFLite
TFLite is a lightweight version of TensorFlow, designed for running machine learning models on mobile, embedded, and IoT devices. It enables on-device inference, meaning models can be executed directly on the device without needing a network connection or relying on cloud-based processing. This reduces latency,…
PyTorch Mobile
PyTorch Mobile is a framework that allows you to run PyTorch models directly on mobile devices, like smartphones and tablets. It enables on-device machine learning inference, meaning the model computations happen locally without needing a network connection to a remote server. This offers benefits like reduced…
Jetson
NVIDIA Jetson is a series of embedded computing systems designed for AI and robotics applications. These systems-on-modules (SoMs) provide high-performance processing capabilities in a compact, energy-efficient form factor, enabling developers to deploy AI models and perform complex computations directly on edge…
LIME
LIME (Local Interpretable Model-agnostic Explanations) is a technique used to understand the predictions of machine learning models by approximating them locally with a more interpretable model. It focuses on explaining individual predictions by perturbing the input data around a specific instance and observing how…
SHAP
SHAP (SHapley Additive exPlanations) is a method used to explain the output of any machine learning model. It uses concepts from game theory to assign each feature a value representing its contribution to the prediction. These values, known as SHAP values, indicate the degree to which each feature contributed to…
Stage 13: Explainable AI
Explainable AI
Explainable AI (XAI) refers to methods and techniques used to make the decisions of machine learning models understandable to humans. It aims to shed light on how a model arrives at a particular prediction, identifying the factors that influenced the outcome. This allows users to understand, trust, and effectively…
Questions about the MLOps Roadmap
Is the MLOps Roadmap free?
Yes. All 62 lessons in this roadmap are free to read on Bibha AI Learning Labs, with no account and no payment. Signing in is optional: it lets you mark lessons as done and keep your progress, and it opens the AI tutor, which runs on your own API key.
How long does the MLOps Roadmap take?
There is no fixed duration. It has 62 lessons in 13 stages, and the time it takes depends on how many hours you give it each week. Nothing is locked, so you can skip what you already know, and your progress is saved once you sign in.
Where should I start the MLOps Roadmap?
Start with stage 1, What is MLOps?, and work down in order. The stages are numbered in the order the roadmap suggests, but every lesson is open, so if you already know What is MLOps? you can go straight to Version Control Systems or anything after it. Each lesson links to free resources for going deeper.
Can I learn MLOps in Hindi?
The lessons are written in English. Once you sign in, you can ask the AI tutor on any lesson to explain it in Hindi or Hinglish. It answers from that lesson’s own text, so the explanation stays on the syllabus. The tutor runs on your own Google, OpenAI or Anthropic key.